Asymptotics of discrete MDL for online prediction
Minimum Description Length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning non-i.i.d. processes by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework, i.e. observations come in one by one, and the predictor is allowed to update his state of mind after each time step. We identify two ways of predicting by MDL for this setup, namely a...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Information Theory, 51:11 (2005) 3780-3795|
|Poland and Hutter Asymptotics of Discrete MDL 2005.pdf||376.32 kB||Adobe PDF|
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